Publications by Mark Bounthavong
Forest plots in R
Forest plots Forest plots are useful when summarizing the invidivual impact of studies on the pooled end points for meta analysis or illustrating the influence of coefficients in a regression model. More importantly, they visualize the direction and magnitude of the associations between the individual studies or coefficients on the outcomes. The ...
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Confounding and Interaction
R tutorial using epitools: Confounding and Effect Modification Mark Bounthavong 9/26/2021 Updated on 01/28/2022 ⊕This tutorial is located on RPubs. ⊕The entire R Markdown code is located on my GitHub page Installing and using epitools This tutorial will center around using the R package epitools to understand confounding and interaction in ep...
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Logistic Regression in R - Part 2 (Goodness of fit)
Introduction In a previous article, we discuss how to construct a logistic regression in R (see previous article). However, we did not discuss how to assess whether the logistic regression fit our data well. In this article, we’ll go over some goodness of fit tests to help determine whether the logistic regression model does a good job of fitti...
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Logistic Regression in R
Introduction When you have a binary outcome (Yes/No), you can use a chi square test to compare the differences in proportions across \(n\) number of groups. For instance, if you had two groups (exposed and unexposed) and a binary outcome (event and no event), you can create a 2 x 2 contingency table and use a chi square test to test if there is a...
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Visualizing linear regression models - Part 2
Introduction In a previous article, we discussed how to construct and visualize linear models using R with the lm() command and the predict3d package. In this article, we will build upon our knowledge of the linear model by checking for model fit to the actual data and assessing whether the model’s residuals violate the assumptions. Some of the...
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Visualizing Linear Regression Models
Introduction Linear regression models allow us to determine if changing the values on a variable is associated with the values of another variable. In other words, if I make a 1-unit change in \(X\), how much does Y change? In fact, linear regression is similar to the algebraic equation for a simple line (\(Y = mx + b\), where \(m\) is the slope,...
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Sample size estimation and Power analysis in R
Sample Size Estimation and Power Analysis Mark Bounthavong 12/29/2021 Introduction ⊕This tutorial is available on RPubs Estimating the sample size for a prospective study is one of the first things that researchers do prior to enrolling patients. Doing this exercise helps the researcher to determine if they could feasibly enroll the necessary n...
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Linear Regression models using R
R tutorial on linear regression model Mark Bounthavong 01/28/2022 ⊕This tutorial is located on Rpubs. ⊕The entire R Markdown code is located on my GitHub page. Introduction ⊕We will Use the diabetes.csv data set, which you can download at this location. Linear regression models (also known as “Ordinary Least Squares” model) allow us to ...
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Survival analysis in R
Survival Analysis in R Mark Bounthavong 2/7/2022 ⊕This tutorial is located on RPubs. ⊕The entire R Markdown code is located on my GitHub page ⊕For this tutorial, you will need the following packages: survival, dplyr, psych, survminer, gmodels, and gtsummary. Introduction A dichotomous variable has two outcomes (Yes or No, Survives or Dies, ...
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